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Lection 2: Genetic variation in transcription networks

Lection 2: Genetic variation in transcription networks. Kristy Harmon, Lauren McIntyre, Marta Wayne, UF Gainsville, UF Gainsville Matt Hahn, Linda Bisson, Kristy Harmon IU Bloomington. Evolution in Levels. Molecular Evolution Quantitative Genetics

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Lection 2: Genetic variation in transcription networks

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  1. Lection 2: Genetic variation in transcription networks Kristy Harmon, Lauren McIntyre, Marta Wayne, UF Gainsville, UF Gainsville Matt Hahn, Linda Bisson, Kristy Harmon IU Bloomington

  2. Evolution in Levels Molecular Evolution Quantitative Genetics Evolution of Phenotype

  3. • Intraspecific Transcriptome Variation • Components of Variation • Dimensionality of Variation • Phenotypic Effects?

  4. Intraspecific Variation Affymetrix microarrays: 13,966 genes Heritability of expression: 663 genes: 0.47 0.39 0.60 cis + trans: 8.7% trans: 6.8% P=0.0397

  5. Male-Female Differences in Splicing v1. Genetics v2. ? v3. Genome Biology

  6. Male-Female Differences in Splicing Total Above Line & sex Line Sex Probe type genes control significance signif. signif. Alter. Trans. 2768 2479 1471 250 1336 Gene family 177 162 118 31 103 Singleton 12912 8265 4602 296 4387 Total 15894 10933 6202 349 5832 % of “above control” n/a 57% 3% 53% Probe type Above Line, sex & probe Line&probe Sex&probe control significance significance significance Altern. Trans. 828 182 26 177 Gene family 91 23 4 22 Total 919 205 30 199 % of “above control” 23% 3%21%

  7. Intraspecific Transcriptome Variation: Components of Variation. Sires Dams

  8. Diallel data summary • Agilent 60b oligos • 9,863 genes were included in analysis (requirements: no multiple splice products, and probes higher than negative controls) • 1,609 X linked genes • 8,213 “autosomal” genes (2, 3 only) • 35 4th chromosome • 6 Y chromosome •Models run for sexes separately; evaluated significance at FDR = 0.20 P = 2GCA + SCA + 2RGCA+ RSCA + 

  9. Conclusions: more genes genetically vary for transcript level in females Male Female X Autosome X Autosome Genetic 192 1337 604 2720 GCA 185 1336 88 630 SCA 1 2 361 1284 rGCA 44 23 1 1 rSCA 2 1 341 1380

  10. GCA, SCA Sires Dams

  11. Conclusions: but more genes have heritable variation in transcript level in males Male Female X Autosome X Autosome Genetic 192 1337 604 2720 GCA 185 1336 88 630 SCA 1 2 361 1284 rGCA 44 23 1 1 rSCA 2 1 341 1380 (and heritable variation is underrepresented on X)

  12. rGCA; rSCA Sires Dams

  13. Conclusions: males possess ~no epistatic variation in transcript levels, while females are overwhelmed with it Male Female X Autosome X Autosome Genetic 192 1337 604 2720 GCA 185 1336 88 630 SCA 1 2 361 1284 rGCA 44 23 1 1 rSCA 2 1 341 1380 (and it is overrepresented on the X chromosome)

  14. Overall “conclusions” • Heritable variation in transcript level is “consistent” with mutation-selection balance; • Dominance / epistatic variation might (?) be consistent with “antagonistic arms race”. Benefit to malesfemales Harmful for femalesmales Dominance condition recessive dominant

  15. • Intraspecific Transcriptome Variation • Components of Variation • Dimensionality of Variation • Phenotypic Effects?

  16. Intraspecific Transcriptome Variation: Phenotypic Effects.9 lines  eggs (5+8h)  Affymetrix chips.

  17. Binding sites of segmentation genes (from Schroeder et al. 2004)

  18. Model Model Fit Parameter Estimate Pr > F / Significance (P) oc = bcd gt kr kni .0152 1.44 -0.24 0.63 -0.60 .006 .185 .082 .097 oc = tor gt kni .0849 -0.03 0.22 0.23 .893 .143 .446 ems = kni <.0001 0.93 .0001 {btd}* cnc = Tor Bcd Gt Kr Kni .0459 -0.30 0.73 -0.51 1.18 -0.34 .140 .341 .052 .026 .321 {hb} Kr = Gt Kni Cad .0050 0.29 0.77 0.03 .093 .022 .834 Gt = Bcd {Gt} Kr Kni .0110 -2.78 {} 0.85 -1.19 .051 .289 .116 Kni = Tor Bcd Kr .0287 -0.19 0.06 0.64 .528 .940 .163 {eve} h = Kr <.0001 1.27 .0001 h = Kr Kni Cad .0054 0.90 0.47 -0.00 .099 .371 .998 h = Gt Cad .0188 0.27 0.47 .094 .009 run = Bcd Kr Kni .0041 -1.69 0.55 -0.58 .036 .220 .149 ftz = Bcd Gt .0018 -0.67 0.48 Cad = Gt Kni {Cad} .0177 -0.57 -0.88 {} .350 .07 6.093 .133 {spl2} {nub} pdm2 = Bcd Kr Cad .0350 0.79 0.61 -0.52 D = Bcd Gt Kr Kni Cad .0163 1.61 0.48 1.36 1.44 0.14 .647 .315 .205 .476 .315 .157 .141 .714 {hb} {eve} Kr = Bcd Gt Kni Cad .0228 -0.34 0.24 0.71 0.07 h = Gt Cad .0663 0.16 -0.49 .814 .386 .114 .768 .674 .186 Kr = Tor Bcd Gt Kni .0234 -0.06 0.05 0.27 0.71 h = Bcd Kr Kni Cad .0016 2.52 1.54 0.42 -0.43 .839 .965 .338 .120 .025 .008 .189 .054 Gt = Kr Kni .0248 2.04 -1.04 h = Kr Kni Cad .0054 0.90 0.46 -0.00 .035 .273 .099 .371 .998 {eve} h = Kr Kni .0008 0.91 0.47 run = Tor Bcd Kr Kni Cad .028 0.42 -0.13 0.80 -0.53 -0.58 .051 .317 .363 .910 .129 .208 .199 odd = Bcd Kr Cad <0.0001 -0.61 0.52 -0.29 run = Kr Cad .0023 0.41 -0.35 .043 .001 .002 .156 .054 Gt = Kr Cad .0284 0.75 -0.36 run = Bcd Kr Kni Cad .0097 -0.78 0.67 -0.64 -0.26 .241 .331 .422 .147 .114 .248 Kni = Bcd Kr {Kni} .0085 -0.16 0.70 {} odd = Gt Kr Kni Cad <0.0001 0.11 0.54 0.06 -0.35 .829 .107 .085 .010 .642 .001

  19. Factor Analysis for Expression Data Factor is a linear combination Measurements “load” on of measurements factors In every genotype, the value of the factor can be calculated and correlated with the trait value  genes with high “loads”

  20. How many dimensions? (among 9 genotypes)

  21. Flies  Yeast • No tissue problems; • Bunch of phenotypes  ethanol; temperature; sulfates; …. 30 genotypes (4 reps including dye swaps); Log phase; Agilent arrays.

  22. Transcripts to explore or confirm. Does variation in TF expression level account for variation in expression of targets? ADR1  Transcription Factor  ABF1 ADR1 AFT2 CHA4 CRZ1 CTH1 DAL80 DAL80 FAP1 FHL1 FKH1 FKH2 FZF1 GAT2 GAT3 GAT4 GIS1 GIS2 GLN3 GLN3 GZF3 HAC1 HMS1 HSF1 IFH1 MAL13 MAL33 MBP1 YER028C MSN2 MSN4 NDT80 PHO2 PHO2 PHO4 PLM2 PPR1 RAP1 RCS1 RCS1 RDR1 RDS1 RDS2 RDS3 SFP1 SFP1 SKN7 SKN7 SMP1 SOK2 STB5 STE12 STP1 SUT1 SWI4 TBF1 TOS4 TOS8 TYE7 TYE7 UGA3 WAR1 XBP1 YAP1 YAP1 YAP3 ZMS1 Factors: 6 (how much variance is explained) Correlations: ADR1 X Factor 1: 0.581 (P=0.0008) ADR1 X Factor 2: 0.656 (P<0.0001) ADR1 X Factor ? – non significant.

  23. Exploratory Factor Analysis • ADR1  Transcription Factor  Which of the regulated genes are real? Loading Gene: Factor 1 Factor 2 ABF1 -17 -7 AFT2 -4 54 * CHA4 -5 1 CRZ1 -7 54* FZF1 50 * 10 GAT2 54* 64* GAT3 46* -1 GAT4 65* -21

  24. Confirmatory Factor Analysis, Structural Equation 31 Exogenous variables12 Endogenous parameters

  25. Yeast Pathways Are “Well”-Established V1 = 0.54V2 + E1 t = 10.18 V2 = 0.61V3 + E2 t = 9.69 V3 = 6.60V4 – 2.57V5 +E3 t = 4.19 t=1.95  model phenotype; Direct selection on V1  indirect selection.

  26. Yeast Confirmatory Factor Analysis • Genetic / metabolic Small natural mutations introduce are networks non-linear; linear deviations (natural variation is mostly additive); • To parameterize the Nature supplies unlimited number of model, many degrees of segregating alleles; freedom are required; • Pathways are never Latent variables can be used complete, many variables instead. can not be measured.

  27. Yeast Confirmatory Factor Analysis, Latent Variables 33 Exogenous variables13 Endogenous parameters

  28. 1. Intraspecific Transcriptome Variation • 10-20% genes significantly vary in transcript level among populations, heritability is high; • there is a similar level of variation for alternative splicing levels; • transcriptome variation is profoundly: heritable in males, epistatic in females; • antagonistic effects of alleles on two sexes may contribute to the overabundance of X-linked variation; • factors explaining ITV is a promising analysis to identify genes and networks controlling QTs.

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